Beyond population engagement: understanding counterinsurgency

The US military has made considerable progress in developing counterinsurgency (COIN) strategy and doctrine, including the publication of Army Field Manual 3-24 and the military's successes in working with the population to stem the insurgency in Iraq. The short-term goals of COIN are now fairly well understood: engage the population and win their support. Whichever side wins the support of the population--either the host nation (and US forces that support it) or the insurgents--wins the battle. The battle is not the war, however. the long-term goal of a counterinsurgency campaign requires the creation of a functioning state, a government that can stand on its own, provide for its citizens, and promote regional and international stability; this achievement is victory in a counterinsurgency. Transitioning from the short-term success of population engagement to long-term viability of the host nation is far more difficult and less understood

Response of bacterioplankton community structures to hydrological conditions and anthropogenic pollution in contrasting subtropical environments

Bacterioplankton community structures under contrasting subtropical marine environments (Hong Kong waters) were analyzed using 16S rRNA gene denaturing gradient gel electrophoresis (DGGE) and subsequent sequencing of predominant bands for samples collected bimonthly from 2004 to 2006 at five stations. Generally, bacterial abundance was significantly higher in the summer than in the winter. The general seasonal variations of the bacterial community structure, as indicated by cluster analysis of the DGGE pattern, were best correlated with temperature at most stations, except for the station close to a sewage discharge outfall, which was best explained by pollution-indicating parameters (e.g. biochemical oxygen demand). Anthropogenic pollutions appear to have affected the presence and the intensity of DGGE bands at the stations receiving discharge of primarily treated sewage. The relative abundance of major bacterial species, calculated by the relative intensity of DGGE bands after PCR amplification, also indicated the effects of hydrological or seasonal variations and sewage discharges. For the first time, a systematic molecular fingerprinting analysis of the bacterioplankton community composition was carried out along the environmental and pollution gradient in a subtropical marine environment, and it suggests that hydrological conditions and anthropogenic pollutions altered the total bacterial community as well as the dominant bacterial groups. © 2009 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.published_or_final_versio

The spatial and temporal distribution of heavy metals in sediments of Victoria Harbour, Hong Kong

Author name used in this publication: Chloe Wing-yee TangAuthor name used in this publication: Carman Ching-man Ip2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: http://sysbio.icm.edu.pl/secstruct and http://code.google.com/p/cmater-bioinfo

Molecular Prognostic Prediction for Locally Advanced Nasopharyngeal Carcinoma by Support Vector Machine Integrated Approach

BACKGROUND:Accurate prognostication of locally advanced nasopharyngeal carcinoma (NPC) will benefit patients for tailored therapy. Here, we addressed this issue by developing a mathematical algorithm based on support vector machine (SVM) through integrating the expression levels of multi-biomarkers. METHODOLOGY/PRINCIPAL FINDINGS:Ninety-seven locally advanced NPC patients in a randomized controlled trial (RCT), consisting of 48 cases serving as training set and 49 cases as testing set of SVM models, with 5-year follow-up were studied. We designed SVM models by selecting the variables from 38 tissue molecular biomarkers, which represent 6 tumorigenesis signaling pathways, and 3 EBV-related serological biomarkers. We designed 3 SVM models to refine prognosis of NPC with 5-year follow-up. The SVM1 displayed highly predictive sensitivity (sensitivity, specificity were 88.0% and 81.9%, respectively) by integrating the expression of 7 molecular biomarkers. The SVM2 model showed highly predictive specificity (sensitivity, specificity were 84.0% and 94.5%, respectively) by grouping the expression level of 12 molecular biomarkers and 3 EBV-related serological biomarkers. The SVM3 model, constructed by combination SVM1 with SVM2, displayed a high predictive capacity (sensitivity, specificity were 88.0% and 90.3%, respectively). We found that 3 SVM models had strong power in classification of prognosis. Moreover, Cox multivariate regression analysis confirmed these 3 SVM models were all the significant independent prognostic model for overall survival in testing set and overall patients. CONCLUSIONS/SIGNIFICANCE:Our SVM prognostic models designed in the RCT displayed strong power in refining patient prognosis for locally advanced NPC, potentially directing future target therapy against the related signaling pathways

Structural, thermal and dissolution properties of MgO- and CaO-containing borophosphate glasses: effect of Fe2O3 addition

This paper investigated manufacture of high-durability phosphate glass fibres for biomedical applications. Five different borophosphate glass formulations in the systems of 45P2O5–5B2O3–5Na2O–(29 − x)CaO–16MgO–(x)Fe2O3 and 45P2O5–5B2O3–5Na2O–24CaO–(21 − x)MgO–(x)Fe2O3 where x = 5, 8 and 11 mol% were produced via melt quenching. The compositions and amorphous nature of the glasses were confirmed by ICP-MS and XRD, respectively. FTIR results indicated depolymerisation of the phosphate chains with a decrease in Q2 units with increasing Fe2O3 content. DSC analyses showed an increase in Tg by ~5 °C with an increment of 3 mol% in Fe2O3 content. The thermal properties were also used to calculate processing window (i.e. Tc,ons—Tg) and another parameter, Kgl, to determine the suitability for fibre drawing directly from melt, which equals (Tc,ons—Tg)/(Tl—Tc,ons). The degradation study conducted in PBS solution at 37 °C showed a decrease of 25–47% in degradation rate with increasing Fe2O3 content. This confirmed that the chemical durability of the glasses had increased, which was suggested to be due to Fe2O3 addition. Furthermore, the density measured via Archimedes method revealed a linear increase with increasing Fe2O3 content

Recruitment in the sea: bacterial genes required for inducing larval settlement in a polychaete worm

Metamorphically competent larvae of the marine tubeworm Hydroides elegans can be induced to metamorphose by biofilms of the bacterium Pseudoalteromonas luteoviolacea strain HI1. Mutational analysis was used to identify four genes that are necessary for metamorphic induction and encode functions that may be related to cell adhesion and bacterial secretion systems. No major differences in biofilm characteristics, such as biofilm cell density, thickness, biomass and EPS biomass, were seen between biofilms composed of P. luteoviolacea (HI1) and mutants lacking one of the four genes. The analysis indicates that factors other than those relating to physical characteristics of biofilms are critical to the inductive capacity of P. luteoviolacea (HI1), and that essential inductive molecular components are missing in the non-inductive deletion-mutant strains